RDA-SHARC’s Open Science Rewards and Incentives Survey

Author

Florencia Grattarola

Published

February 16, 2024

This survey addresses awareness of Open Science activities and existing & expected reward systems in research assessment, in particular for sharing activities, in different contexts.

Quick summary of respondents background: We received 230 responses, from people working in 37 countries, of which 41.3% declare their gender was female, 35.65% male, 1.3% non-binary or gender-queer and 21.74% had no response. Most respondents had ‘Researcher’ as their main job title (40.87%), were affiliated to a ‘University’ (39.13%) or a ‘Research institute’ (37.39%), and had between 10 and 20 years of experience in their field (32.17%).

Section 1

Are you familiar with Open Science?

Code
survey %>%
    group_by(are_you_familiar_with_open_science) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(n/sum(n))) %>%
    rename(`Are you familiar with Open Science?` = are_you_familiar_with_open_science) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))
Are you familiar with Open Science? n %
No 22 10%
Yes 208 90%
Code
# general
survey %>%
    group_by(are_you_familiar_with_open_science) %>%
    count() %>%
    ggplot(aes(y = n, x = "", fill = are_you_familiar_with_open_science)) +
    geom_bar(stat = "identity", position = "fill") +
    scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_brewer(palette = "Set1") +
    theme_light() + labs(fill = "Are you familiar\nwith Open Science?",
    x = "", y = "")

Are you involved in some of the following Open Science activities?

Code
# Select only the likert items in the
# survey
items_involved <- select(survey, starts_with("are_you_involved_in_some_of"))

# Rename the items so that the question
# statement becomes the name
names(items_involved) <- c("Sharing a research manuscript as a preprint",
    "Publishing a paper or monograph book as open access",
    "Preregistration of the study design, methods, hypothesis etc., prior to commencing the research",
    "Open or FAIR data management and sharing (for research data, software, models, algorithms, workflows etc.)",
    "Participation in open peer review (being reviewed or the reviewer)",
    "Participation in public engagement, including citizen or community science",
    "Collaboration via virtual research environments or virtual laboratories",
    "None of the above")

# We don't want this option
items_involved <- items_involved %>%
    select(-`None of the above`)

# A custom function to recode numerical
# responses into ordered factors
likert_recode_yn <- function(x) {
    y <- factor(x, levels = c("No", "Yes"),
        exclude = "N/A")
    return(y)
}

# Transform the items into factors and
# save the data set as a likert object
items_involved <- items_involved %>%
    mutate_all(likert_recode_yn) %>%
    as.data.frame() %>%
    likert::likert()

# percentages
items_involved$results %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position")) %>%
    kableExtra::column_spec(1, width = "30em")
Item No Yes
Sharing a research manuscript as a preprint 47 53
Publishing a paper or monograph book as open access 37 63
Preregistration of the study design, methods, hypothesis etc., prior to commencing the research 84 16
Open or FAIR data management and sharing (for research data, software, models, algorithms, workflows etc.) 47 53
Participation in open peer review (being reviewed or the reviewer) 60 40
Participation in public engagement, including citizen or community science 50 50
Collaboration via virtual research environments or virtual laboratories 72 28
Code
# plot
likert.bar.plot(items_involved, plot.percent.low = FALSE,
    plot.percent.high = FALSE, col = c("#D7B365",
        "#59B4AB"), wrap = 40, plot.percents = TRUE) +
    ggpubr::theme_cleveland() + theme(legend.position = "bottom")

Are you familiar with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles?

Code
survey %>%
    group_by(are_you_familiar_with_the_fair_findable_accessible_interoperable_and_reusable_principles_defined_here) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(n/sum(n))) %>%
    rename(Responses = are_you_familiar_with_the_fair_findable_accessible_interoperable_and_reusable_principles_defined_here) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))
Responses n %
No 60 26%
Yes 170 74%
Code
# general
survey %>%
    group_by(are_you_familiar_with_the_fair_findable_accessible_interoperable_and_reusable_principles_defined_here) %>%
    count() %>%
    ggplot(aes(y = n, x = "", fill = are_you_familiar_with_the_fair_findable_accessible_interoperable_and_reusable_principles_defined_here)) +
    geom_bar(stat = "identity", position = "fill") +
    scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_brewer(palette = "Set1") +
    theme_light() + labs(fill = "Are you familiar\nwith the FAIR principles?",
    x = "", y = "")

Are you involved in some steps of data FAIRification process?

Code
survey %>%
    group_by(are_you_involved_in_some_steps_of_data_fai_rification_process_such_as_those_referred_to_here) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(n/sum(n))) %>%
    rename(Responses = are_you_involved_in_some_steps_of_data_fai_rification_process_such_as_those_referred_to_here) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))
Responses n %
N/A 62 27.0%
No 94 40.9%
Yes 74 32.2%
Code
# general
survey %>%
    rename(response = are_you_involved_in_some_steps_of_data_fai_rification_process_such_as_those_referred_to_here) %>%
    group_by(response) %>%
    count() %>%
    ggplot(aes(x = "", y = n, fill = fct_relevel(response,
        "No", "Yes", "N/A"))) + geom_bar(stat = "identity",
    position = "fill") + scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_brewer(palette = "Set1") +
    theme_light() + labs(fill = "Are you involved in\nsome steps of data\nFAIRification process?",
    x = "", y = "")

Does your institute/organisation have policies on various Open Science activities?

Code
survey %>%
    group_by(does_your_institute_organisation_have_policies_on_various_open_science_activities) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(n/sum(n))) %>%
    rename(Responses = does_your_institute_organisation_have_policies_on_various_open_science_activities) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))
Responses n %
N/A 6 3%
No 98 43%
Yes 126 55%
Code
# general
survey %>%
    rename(response = does_your_institute_organisation_have_policies_on_various_open_science_activities) %>%
    group_by(response) %>%
    count() %>%
    ggplot(aes(x = "", y = n, fill = fct_relevel(response,
        "No", "Yes", "N/A"))) + geom_bar(stat = "identity",
    position = "fill") + scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_brewer(palette = "Set1") +
    theme_light() + labs(fill = "Does your institute/\norganisation have policies\non various Open Science\nactivities?",
    x = "", y = "")

Section 2

Could you please specify to what extent you feel the following activities should be credited / rewarded?

Rewards may include career promotion, grants/funding/prizes, gained credits in a research evaluation procedure, authorship/ contributorship, increased academic visibility.

Code
# Select only the likert items in the
# survey
items <- select(survey, starts_with(c("could_you_please_specify_to_what_extent")))

# Rename the items so that the question
# statement becomes the name
names(items) <- c("Sharing a research manuscript as a preprint",
    "Publishing a paper or monograph book as open access",
    "Preregistration of the study design, methods, hypothesis etc., prior to commencing the research",
    "Open or FAIR data management and sharing (for research data, software, models, algorithms, workflows etc.)",
    "Participation in open peer review (being reviewed or the reviewer)",
    "Participation in public engagement, including citizen or community science",
    "Collaboration via virtual research environments or virtual laboratories")

# A custom function to recode numerical
# responses into ordered factors
likert_recode <- function(x) {
    y <- factor(x, levels = c("Definitely Not",
        "Probably Not", "Possibly", "Very Probably",
        "Definitely"), exclude = "No opinion")
    return(y)
}

# Transform the items into factors and
# save the data set as a likert object
items_likert <- items %>%
    mutate_all(likert_recode) %>%
    as.data.frame() %>%
    likert::likert()

# percentages
items_likert$results %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position")) %>%
    kableExtra::column_spec(1, width = "10em")
Item Definitely Not Probably Not Possibly Very Probably Definitely
Sharing a research manuscript as a preprint 4.21 10.3 31 27 27
Publishing a paper or monograph book as open access 0.89 4.5 16 23 55
Preregistration of the study design, methods, hypothesis etc., prior to commencing the research 3.35 9.1 29 31 28
Open or FAIR data management and sharing (for research data, software, models, algorithms, workflows etc.) 0.90 2.7 14 26 56
Participation in open peer review (being reviewed or the reviewer) 1.81 6.3 30 27 35
Participation in public engagement, including citizen or community science 0.90 4.1 26 24 45
Collaboration via virtual research environments or virtual laboratories 0.50 7.9 46 27 19
Code
# plot
likert.bar.plot(items_likert, wrap = 30,
    centered = F, plot.percent.neutral = TRUE,
    plot.percent.low = TRUE, plot.percent.high = TRUE) +
    ggpubr::theme_cleveland() + theme(legend.position = "bottom")

Section 3

Does your institute/organisation have any initiative or tool which gives credits/rewards for Open Science activities?

Code
survey %>%
    group_by(does_your_institute_organisation_have_any_initiative_or_tool_which_gives_credits_rewards_for_open_science_activities) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(n/sum(n))) %>%
    rename(Responses = does_your_institute_organisation_have_any_initiative_or_tool_which_gives_credits_rewards_for_open_science_activities) %>%
    knitr::kable(digits = 2) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))
Responses n %
No 195 85%
Yes 35 15%
Code
# general
survey %>%
    rename(response = does_your_institute_organisation_have_any_initiative_or_tool_which_gives_credits_rewards_for_open_science_activities) %>%
    group_by(response) %>%
    count() %>%
    ggplot(aes(y = n, x = "", fill = response)) +
    geom_bar(stat = "identity", position = "fill") +
    scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_brewer(palette = "Set1") +
    theme_light() + labs(fill = "Does your institute/organisation\nhave any initiative or tool which\ngives credits/rewards for\nOpen Science activities?",
    x = "", y = "")

Code
# by country
survey %>%
    rename(response = does_your_institute_organisation_have_any_initiative_or_tool_which_gives_credits_rewards_for_open_science_activities,
        country = which_country_do_you_work_in) %>%
    group_by(response, country) %>%
    count() %>%
    filter(response == "Yes") %>%
    ggplot(aes(y = n, x = country, fill = country)) +
    geom_bar(stat = "identity") + scale_fill_brewer(palette = "Spectral") +
    scale_fill_manual(values = colorRampPalette(RColorBrewer::brewer.pal(9,
        "Set1"))(15)) + theme_light() + theme(axis.text.x = element_text(size = rel(0.5))) +
    labs(caption = "Mentioned initiative or tool which gives credits/rewards for Open Science activities by country",
        fill = "", x = "", y = "")

Section 4

How would you want the previously mentioned Open Science activities to be rewarded?

Labels

Code
rewardLabels <- survey %>%
    rename(labels = suggested_rewards_categorised_according_to_shar_cs_rewarding_terminology) %>%
    separate_rows(labels, sep = ";") %>%
    mutate(labels = str_squish(labels)) %>%
    filter(!is.na(labels) & labels != "") %>%
    mutate(labels = ifelse(labels == "good science'",
        "'good science'", labels)) %>%
    mutate(labels = ifelse(labels == "funding/grants for OS activitie",
        "funding/grants for OS activities",
        labels)) %>%
    mutate(labels = ifelse(labels == "OS indicators in research evaluation processes",
        "OS indicators in research evaluation and/or career progression processes",
        labels))

rewardLabels %>%
    group_by(labels) %>%
    count() %>%
    arrange(desc(n)) %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(`SHARC Rewards Terminology` = labels) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))
SHARC Rewards Terminology n %
OS indicators in research evaluation and/or career progression processes 126 40.3%
funding/grants for OS activities 49 15.7%
awards/bonuses 33 10.5%
research visibility indicators 26 8.3%
authorship/contributorship 20 6.4%
capacity building for OS (e.g., training, raising awareness, provision of IT tools) 10 3.2%
research reputation 9 2.9%
acknowledgement/citation 8 2.6%
support through regulations and policy mandates 8 2.6%
collaboration (e.g., joint research, co-authorship) 7 2.2%
no specific reward 5 1.6%
contribution to ‘good’ science, research quality and integrity 3 1.0%
OS certifications/badges 2 0.6%
science as a public good 2 0.6%
CRediT taxonomy 1 0.3%
OS activities included in working hours 1 0.3%
championships/contests 1 0.3%
financial contribution to reviewers of OA journals 1 0.3%
punishment for 'closed' science 1 0.3%
Code
rewardLabels %>%
    filter(!is.na(labels)) %>%
    group_by(labels) %>%
    count() %>%
    ggplot(aes(x = "", y = n, fill = fct_reorder(labels,
        n))) + geom_bar(stat = "identity",
    position = "fill") + guides(fill = guide_legend(ncol = 1)) +
    scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_manual(values = colorRampPalette(RColorBrewer::brewer.pal(9,
        "Set1"))(19)) + theme_light() + labs(fill = "SHARC Rewards\nTerminology",
    x = "", y = "") + theme(legend.position = "right")

Code
most_common_rewards <- c("OS indicators in research evaluation and/or career progression processes",
    "funding/grants for OS activities", "awards/bonuses",
    "research visibility indicators", "authorship/contributorship")

rewardLabels %>%
    filter(!is.na(labels)) %>%
    mutate(labels = ifelse(!labels %in% most_common_rewards,
        "others", labels)) %>%
    group_by(labels) %>%
    count() %>%
    ggplot(aes(x = "", y = n, fill = fct_relevel(fct_reorder(labels,
        n), "others"))) + geom_bar(stat = "identity",
    position = "fill") + guides(fill = guide_legend(ncol = 1)) +
    scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_brewer(palette = "Set1") +
    ggpubr::theme_cleveland() + labs(fill = "SHARC Rewards\nTerminology",
    x = "", y = "") + theme(legend.position = "right")

Respondents’ background

Gender

These are the different responses and how they were classified. I now added ‘non-binary’ as a different category and tagged all other as ‘other responses’.

Code
survey %>%
    distinct(your_gender, gender) %>%
    kable()
Gender responses
your_gender gender
Femenino female
M male
- no response
masculino male
mujer female
femenino female
Masculino male
Mujer female
F female
NA no response
Female female
female female
FEMENINO female
mujer cis female
male male
Male male
Woman female
male
female
féminin female
woman female
Why does that matter no response
Genderqueer non-binary / gender-queer
f female
Female (she/her) female
Female cis female
Cis female female
non-binary non-binary / gender-queer
Non-binary non-binary / gender-queer
feminine female
Femail female
not relevant no response
male; trans man male
  • Your gender
Code
survey %>%
    group_by(gender) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(`Declared gender` = gender) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))
Declared gender n %
female 95 41.3%
male 82 35.7%
no response 50 21.7%
non-binary / gender-queer 3 1.3%
Code
survey %>%
    group_by(gender) %>%
    count() %>%
    ggplot(aes(x = "", y = n, fill = gender)) +
    geom_bar(stat = "identity", position = "fill") +
    scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_brewer(palette = "Dark2") +
    theme_light() + labs(fill = "Gender",
    x = "", y = "")

Code
survey %>%
    rename(response = which_country_do_you_work_in) %>%
    mutate(response_x = ifelse(response !=
        "Korea, Rep." & response != "France" &
        response != "United States" & response !=
        "Uruguay", "Other countries", response)) %>%
    group_by(response_x) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(Country = response_x) %>%
    arrange(desc(n)) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))
Country n %
Other countries 86 37.4%
France 49 21.3%
United States 42 18.3%
Korea, Rep. 41 17.8%
Uruguay 12 5.2%

Country

  • Which country do you work in?
Code
survey %>%
    group_by(which_country_do_you_work_in) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(Country = which_country_do_you_work_in) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))
Country n %
Albania 1 0.4%
Argentina 11 4.8%
Belgium 2 0.9%
Brazil 5 2.2%
Canada 1 0.4%
Chile 1 0.4%
China 2 0.9%
Colombia 2 0.9%
Croatia 1 0.4%
Czech Republic 1 0.4%
Denmark 2 0.9%
Estonia 1 0.4%
Finland 1 0.4%
France 49 21.3%
Germany 9 3.9%
Greece 1 0.4%
India 2 0.9%
Ireland 3 1.3%
Italy 2 0.9%
Korea, Rep. 41 17.8%
Lithuania 3 1.3%
Luxembourg 1 0.4%
Netherlands 7 3.0%
New Zealand 1 0.4%
Nigeria 3 1.3%
Poland 1 0.4%
Portugal 2 0.9%
Serbia 2 0.9%
Slovenia 1 0.4%
Spain 4 1.7%
Sweden 2 0.9%
Switzerland 1 0.4%
Turkey 1 0.4%
United Arab Emirates 1 0.4%
United Kingdom 8 3.5%
United States 42 18.3%
Uruguay 12 5.2%
Code
# which_country_do_you_work_in
survey %>%
    rename(response = which_country_do_you_work_in) %>%
    group_by(response) %>%
    count() %>%
    ggplot(aes(x = "", y = n, fill = fct_reorder(response,
        desc(n)))) + geom_bar(stat = "identity") +
    scale_fill_manual(values = colorRampPalette(RColorBrewer::brewer.pal(9,
        "Set1"))(37)) + theme_light() + labs(caption = "Which country do you work in?",
    fill = "", x = "", y = "")

Code
# Korea, France, USA, Uruguay, and
# Other countries
survey %>%
    rename(response = which_country_do_you_work_in) %>%
    mutate(response_x = ifelse(response !=
        "Korea, Rep." & response != "France" &
        response != "United States" & response !=
        "Uruguay", "Other countries", response)) %>%
    group_by(response_x) %>%
    count() %>%
    ggplot(aes(x = "", y = n, fill = fct_relevel(fct_reorder(response_x,
        n), "Other countries")), show.legend = FALSE) +
    geom_bar(stat = "identity", position = "fill") +
    scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_brewer(palette = "Spectral") +
    labs(fill = "") + theme_light() + labs(caption = "Which country do you work in?",
    x = "", y = "")

Code
survey %>%
    rename(response = which_country_do_you_work_in) %>%
    mutate(response_x = ifelse(response !=
        "Korea, Rep." & response != "France" &
        response != "United States" & response !=
        "Uruguay", "Other countries", response)) %>%
    group_by(response_x) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(Country = response_x) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))
Country n %
France 49 21.3%
Korea, Rep. 41 17.8%
Other countries 86 37.4%
United States 42 18.3%
Uruguay 12 5.2%

Map

Code
# map plot
world_survey %>%
    st_cast("POLYGON") %>%
    mutate(responses = ifelse(name == "France" &
        grepl("-52", geometry), NA, responses)) %>%
    mutate(name = ifelse(name == "France" &
        is.na(responses), "French Guyana",
        name)) %>%
    group_by(name) %>%
    dplyr::summarise(responses = max(responses)) %>%
    ggplot() + geom_sf(aes(fill = responses),
    size = 0.1) + scale_fill_fermenter(palette = "Spectral",
    breaks = c(0, 1, 2, 3, 4, 5, 10, 15,
        20, 25, 39), na.value = "grey95") +
    theme_minimal() + labs(fill = "N")

Job title

  • Main position/job title: which of the following are you now?
Code
survey %>%
    rename(response = main_position_job_title_which_of_the_following_are_you_now) %>%
    group_by(response) %>%
    count() %>%
    arrange(desc(n)) %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(`Job title` = response) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))
Job title n %
Researcher 94 40.9%
Professor 35 15.2%
Other, please specify in the comment box 27 11.7%
Data specialist 22 9.6%
Graduate student (Master, PhD) 22 9.6%
Postdoc 15 6.5%
Policy officer 10 4.3%
Software engineer 5 2.2%

Experience in the field

  • How long have you been working in your field?
Code
survey %>%
    group_by(how_long_have_you_been_working_in_your_field) %>%
    count() %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(`Experience in the field` = how_long_have_you_been_working_in_your_field) %>%
    mutate(index = ifelse(`Experience in the field` ==
        "> 20 years", 1, ifelse(`Experience in the field` ==
        "10-20 years", 2, ifelse(`Experience in the field` ==
        "5-10 years", 3, ifelse(`Experience in the field` ==
        "< 5 years", 4, 5))))) %>%
    arrange(index) %>%
    select(-index) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))
Experience in the field n %
> 20 years 61 26.5%
10-20 years 74 32.2%
5-10 years 56 24.3%
< 5 years 38 16.5%
NA 1 0.4%
Code
survey %>%
    rename(response = how_long_have_you_been_working_in_your_field) %>%
    group_by(response) %>%
    count() %>%
    filter(!is.na(response)) %>%
    ggplot(aes(x = "", y = n, fill = fct_relevel(response,
        "> 20 years", "10-20 years", "5-10 years",
        "< 5 years"))) + geom_bar(stat = "identity",
    position = "fill") + scale_y_continuous(labels = scales::percent_format()) +
    scale_fill_brewer(palette = "Spectral") +
    theme_light() + labs(fill = "Years of experience",
    x = "", y = "")

Affiliation type of organisation

  • Please select the type of organisation you are primarily affiliated with
Code
survey %>%
    rename(response = please_select_the_type_of_organisation_you_are_primarily_affiliated_with) %>%
    group_by(response) %>%
    count() %>%
    arrange(desc(n)) %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(`Type of organisation` = response) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))
Type of organisation n %
University 90 39.1%
Research institute 86 37.4%
Government agency 29 12.6%
Other, please specify 12 5.2%
NGO / NPO 6 2.6%
International organisation 5 2.2%
Publisher (e.g., scholarly societies) 2 0.9%

Disciplinary fields

  • What are your main disciplinary fields (up to 3; e.g., computer science, physics, biology, medicine)?

This plot is generated by classifying the disciplines according to Wikipedia’s Outline of academic disciplines.

Code
# disciplines
disciplines <- survey %>%
    rename(disciplines = starts_with("what_are_your_main_disciplinary_fields")) %>%
    select(response_id, disciplines) %>%
    separate_rows(disciplines, sep = ";") %>%
    mutate(disciplines = str_squish(disciplines) %>%
        str_to_lower())

new_wiki_disciplines <- read_csv("data/disciplines.csv")
disciplines_for_plot <- left_join(disciplines,
    new_wiki_disciplines)

disciplines_for_plot %>%
    filter(!is.na(subfield)) %>%
    group_by(field) %>%
    count() %>%
    arrange(desc(n)) %>%
    ungroup() %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(Field = field) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))
Field n %
Applied science 78 39.0%
Natural science 59 29.5%
Formal science 32 16.0%
Social science 23 11.5%
Humanities 8 4.0%
Code
disciplines_for_plot %>%
    filter(!is.na(subfield)) %>%
    group_by(field, subfield) %>%
    count() %>%
    arrange(field, desc(n)) %>%
    ungroup() %>%
    head(n = 25) %>%
    mutate(`%` = (scales::label_percent())(round(n/sum(n),
        3))) %>%
    rename(Field = field, `Sub-field` = subfield) %>%
    kableExtra::kbl(booktabs = T) %>%
    kableExtra::kable_styling(latex_options = c("striped",
        "hold_position"))
Field Sub-field n %
Applied science Medicine and health 21 17.8%
Applied science Library and museum studies 10 8.5%
Applied science Law 8 6.8%
Applied science Public administration:Public policy 8 6.8%
Applied science Environmental studies and forestry 5 4.2%
Applied science Business 4 3.4%
Applied science Engineering and technology: Chemical engineering 3 2.5%
Applied science Engineering and technology: Materials science 3 2.5%
Applied science Journalism, media studies and communication 3 2.5%
Applied science Education 2 1.7%
Applied science Engineering and technology: Mechanical engineering 2 1.7%
Applied science Agriculture 1 0.8%
Applied science Architecture and design 1 0.8%
Applied science Engineering and technology 1 0.8%
Applied science Engineering and technology: Civil engineering 1 0.8%
Applied science Engineering and technology: Educational technology 1 0.8%
Applied science Engineering and technology: Electrical engineering 1 0.8%
Applied science Engineering and technology: Mechanical 1 0.8%
Applied science Engineering and technology: Systems science 1 0.8%
Applied science Public administration 1 0.8%
Formal science Computer science 30 25.4%
Formal science Mathematics 2 1.7%
Humanities Philosophy 5 4.2%
Humanities Languages and literature 2 1.7%
Humanities History 1 0.8%
Code
disciplines_for_plot %>%
    filter(!is.na(subfield)) %>%
    group_by(field, subfield) %>%
    count() %>%
    PieDonut(aes(field, subfield, count = n),
        showPieName = F, showRatioThreshold = 0.02,
        labelposition = 1, ratioByGroup = F)

List of disciplines